A General-Purpose Compute-in-Memory Processor Combining CPU and Deep Learning with Elevated CPU Efficiency and Enhanced Data Locality

Abstract

This work presents a general-purpose compute-in-memory (GPCIM) processor combining DNN operations and vector CPU. Utilizing special reconfigurability, dataflow, and instruction set, the 65nm test chip demonstrates a 28.5 TOPS/W DNN macro efficiency and a best-in-class peak CPU efficiency of 802GOPS/W. Due to a data locality flow, 37% to 55% end-to-end latency improvement on AI-related applications is achieved by eliminating inter-core data transfer.